Factors associated with Anaplasma spp. seroprevalence among dogs in the United States

Abstract

Background: Dogs in the United States are hosts to a diverse range of ticks and tick-borne pathogens, including A. phagocytophilum, an important emerging canine and human pathogen. Previously, a Companion Animal Parasite Council (CAPC)-sponsored workshop proposed factors purported to be associated with the infection risk for tick transmitted pathogens in dogs in the United States, including climate conditions, socioeconomic characteristics, local topography, and vector distribution.

Methods: Approximately four million test results from routine veterinary diagnostic tests from 2011–2013, which were collected on a county level across the contiguous United States, are statistically analyzed with the proposed factors via logistic regression and generalized estimating equations. Spatial prevalence maps of baseline Anaplasma spp. prevalence are constructed from Kriging and head-banging smoothing methods.

Results: All of the examined factors, with the exception of surface water coverage, were significantly associated with Anaplasma spp. prevalence. Overall, Anaplasma spp. prevalence increases with increasing precipitation and forestation coverage and decreases with increasing temperature, population density, relative humidity, and elevation. Interestingly, socioeconomic status and deer/vehicle collisions were positively and negatively correlated with canine Anaplasma seroprevalence, respectively. A spatial map of the canine Anaplasma hazard is an auxiliary product of the analysis. Anaplasma spp. prevalence is highest in New England and the Upper Midwest.

Conclusions: The results from the two posited statistical models (one that contains an endemic areas assumptionand one that does not) are in general agreement, with the major difference being that the endemic areas model estimates a larger prevalence in Western Texas, New Mexico, and Colorado. As A. phagocytophilum is zoonotic, the results of this analysis could also help predict areas of high risk for human exposure to this pathogen.